from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2021-12-07 14:04:47.181766
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64('2020-12-27'),
'orange', 'inside top left'),
'Weihnachten 2020': (np.datetime64('2020-12-24'), np.datetime64('2020-12-27'),
'blue', 'inside top left'),
'3. Lockdown': (np.datetime64('2020-12-27'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Tue, 07, Dec, 2021
Time: 14:04:53
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -47.3951
Nobs: 498.000 HQIC: -47.8574
Log likelihood: 5721.17 FPE: 1.21932e-21
AIC: -48.1560 Det(Omega_mle): 1.01955e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.387046 0.081671 4.739 0.000
L1.Burgenland 0.093805 0.044397 2.113 0.035
L1.Kärnten -0.116250 0.022770 -5.105 0.000
L1.Niederösterreich 0.166491 0.091995 1.810 0.070
L1.Oberösterreich 0.125945 0.093340 1.349 0.177
L1.Salzburg 0.282072 0.047599 5.926 0.000
L1.Steiermark 0.013776 0.061464 0.224 0.823
L1.Tirol 0.107065 0.049645 2.157 0.031
L1.Vorarlberg -0.085731 0.043739 -1.960 0.050
L1.Wien 0.033179 0.083572 0.397 0.691
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.019539 0.180979 0.108 0.914
L1.Burgenland -0.051481 0.098382 -0.523 0.601
L1.Kärnten 0.036716 0.050458 0.728 0.467
L1.Niederösterreich -0.221526 0.203855 -1.087 0.277
L1.Oberösterreich 0.480089 0.206835 2.321 0.020
L1.Salzburg 0.312768 0.105476 2.965 0.003
L1.Steiermark 0.098843 0.136200 0.726 0.468
L1.Tirol 0.308114 0.110010 2.801 0.005
L1.Vorarlberg 0.008544 0.096924 0.088 0.930
L1.Wien 0.019070 0.185191 0.103 0.918
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.228422 0.041491 5.505 0.000
L1.Burgenland 0.090680 0.022555 4.020 0.000
L1.Kärnten -0.004704 0.011568 -0.407 0.684
L1.Niederösterreich 0.220577 0.046735 4.720 0.000
L1.Oberösterreich 0.166957 0.047419 3.521 0.000
L1.Salzburg 0.035707 0.024181 1.477 0.140
L1.Steiermark 0.025441 0.031225 0.815 0.415
L1.Tirol 0.075444 0.025221 2.991 0.003
L1.Vorarlberg 0.056374 0.022221 2.537 0.011
L1.Wien 0.106764 0.042456 2.515 0.012
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.163599 0.040408 4.049 0.000
L1.Burgenland 0.043378 0.021966 1.975 0.048
L1.Kärnten -0.012382 0.011266 -1.099 0.272
L1.Niederösterreich 0.148319 0.045516 3.259 0.001
L1.Oberösterreich 0.346758 0.046181 7.509 0.000
L1.Salzburg 0.099853 0.023550 4.240 0.000
L1.Steiermark 0.107168 0.030410 3.524 0.000
L1.Tirol 0.086245 0.024563 3.511 0.000
L1.Vorarlberg 0.054444 0.021641 2.516 0.012
L1.Wien -0.037769 0.041349 -0.913 0.361
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.164025 0.077932 2.105 0.035
L1.Burgenland -0.041077 0.042364 -0.970 0.332
L1.Kärnten -0.036364 0.021728 -1.674 0.094
L1.Niederösterreich 0.126248 0.087782 1.438 0.150
L1.Oberösterreich 0.188107 0.089065 2.112 0.035
L1.Salzburg 0.254668 0.045419 5.607 0.000
L1.Steiermark 0.072577 0.058649 1.237 0.216
L1.Tirol 0.130488 0.047371 2.755 0.006
L1.Vorarlberg 0.105675 0.041736 2.532 0.011
L1.Wien 0.039119 0.079745 0.491 0.624
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.083972 0.061775 1.359 0.174
L1.Burgenland 0.014932 0.033581 0.445 0.657
L1.Kärnten 0.051491 0.017223 2.990 0.003
L1.Niederösterreich 0.176845 0.069583 2.541 0.011
L1.Oberösterreich 0.337960 0.070600 4.787 0.000
L1.Salzburg 0.049828 0.036003 1.384 0.166
L1.Steiermark -0.007200 0.046490 -0.155 0.877
L1.Tirol 0.123059 0.037550 3.277 0.001
L1.Vorarlberg 0.059256 0.033084 1.791 0.073
L1.Wien 0.111708 0.063212 1.767 0.077
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.171423 0.074973 2.286 0.022
L1.Burgenland 0.011564 0.040756 0.284 0.777
L1.Kärnten -0.060842 0.020903 -2.911 0.004
L1.Niederösterreich -0.112787 0.084450 -1.336 0.182
L1.Oberösterreich 0.232073 0.085685 2.708 0.007
L1.Salzburg 0.037907 0.043695 0.868 0.386
L1.Steiermark 0.263977 0.056423 4.679 0.000
L1.Tirol 0.489383 0.045573 10.738 0.000
L1.Vorarlberg 0.071789 0.040152 1.788 0.074
L1.Wien -0.101545 0.076718 -1.324 0.186
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.141306 0.082865 1.705 0.088
L1.Burgenland -0.013146 0.045046 -0.292 0.770
L1.Kärnten 0.064149 0.023103 2.777 0.005
L1.Niederösterreich 0.170884 0.093339 1.831 0.067
L1.Oberösterreich -0.075506 0.094703 -0.797 0.425
L1.Salzburg 0.221848 0.048294 4.594 0.000
L1.Steiermark 0.133869 0.062362 2.147 0.032
L1.Tirol 0.049857 0.050370 0.990 0.322
L1.Vorarlberg 0.142501 0.044378 3.211 0.001
L1.Wien 0.168120 0.084793 1.983 0.047
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.457181 0.045787 9.985 0.000
L1.Burgenland -0.000763 0.024890 -0.031 0.976
L1.Kärnten -0.013271 0.012766 -1.040 0.299
L1.Niederösterreich 0.176256 0.051574 3.418 0.001
L1.Oberösterreich 0.267625 0.052328 5.114 0.000
L1.Salzburg 0.018770 0.026685 0.703 0.482
L1.Steiermark -0.013725 0.034458 -0.398 0.690
L1.Tirol 0.069523 0.027832 2.498 0.012
L1.Vorarlberg 0.056507 0.024521 2.304 0.021
L1.Wien -0.016340 0.046852 -0.349 0.727
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.025788 0.089650 0.150735 0.136740 0.061960 0.080921 0.016616 0.205154
Kärnten 0.025788 1.000000 -0.037185 0.127413 0.047690 0.072712 0.456517 -0.082343 0.093913
Niederösterreich 0.089650 -0.037185 1.000000 0.276492 0.096780 0.251793 0.050670 0.140503 0.243989
Oberösterreich 0.150735 0.127413 0.276492 1.000000 0.189743 0.284272 0.161315 0.123230 0.178212
Salzburg 0.136740 0.047690 0.096780 0.189743 1.000000 0.118640 0.060257 0.108762 0.062730
Steiermark 0.061960 0.072712 0.251793 0.284272 0.118640 1.000000 0.132036 0.086886 0.004326
Tirol 0.080921 0.456517 0.050670 0.161315 0.060257 0.132036 1.000000 0.062877 0.128101
Vorarlberg 0.016616 -0.082343 0.140503 0.123230 0.108762 0.086886 0.062877 1.000000 -0.012655
Wien 0.205154 0.093913 0.243989 0.178212 0.062730 0.004326 0.128101 -0.012655 1.000000